Penghang Shuai

Papers

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About

Penghang Shuai is an emerging researcher at the intersection of artificial intelligence and bio-inspired robotics, with a primary focus on reinforcement learning for autonomous underwater systems. His most notable contribution is a comprehensive 2024 survey on reinforcement learning methods in robotic fish, which systematically reviews how model-free control strategies can overcome the challenges of dynamic modeling in biomimetic vehicles. While still early in his career—reflected in the paper’s initial citation count—this work provides a crucial roadmap for integrating adaptive learning algorithms into flexible, fish-like platforms, addressing key issues in locomotion efficiency and environmental interaction. Shuai’s research bridges the gap between theoretical reinforcement learning advances and practical robotic applications, offering a foundation for future studies in autonomous underwater exploration and environmental monitoring. His survey stands as a valuable resource for students and researchers entering this interdisciplinary field, highlighting both current methodologies and open challenges. As the demand for intelligent, self-learning underwater robots grows, Shuai’s work positions him as a promising voice in shaping how these systems achieve robust, adaptive control in complex aquatic environments.